Support Vector Machine Classification of Drunk Driving Behaviour
Bibliographic Data
| ID | 15511160 |
|---|---|
| Authors | Huiqin Chen (0000-0001-8319-7141, Hunan University, corresponding author), Lei Chen (0009-0003-4632-4932, Hangzhou Dianzi University) |
| Year | 2017 |
| Volume | 14 |
| Issue | 1 |
| Pages | 108-108 |
| Publication date | 2017-01-23 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph14010108 |
| PMID | 28125006 |
| OpenAlex | W2580595058 |
| Language | EN |
| Citations received | 2 |
| References cited | 38 |
Alcohol is the root cause of numerous traffic accidents due to its pharmacological action on the human central nervous system. This study conducted a detection process to distinguish drunk driving from normal driving under simulated driving conditions. The classification was performed by a support vector machine (SVM) classifier trained to distinguish between these two classes by integrating both driving performance and physiological measurements. In addition, principal component analysis was conducted to rank the weights of the features. The standard deviation of R-R intervals (SDNN), the root mean square value of the difference of the adjacent R-R interval series (RMSSD), low frequency (LF), high frequency (HF), the ratio of the low and high frequencies (LF/HF), and average blink duration were the highest weighted features in the study. The results show that SVM classification can successfully distinguish drunk driving from normal driving with an accuracy of 70%. The driving performance data and the physiological measurements reported by this paper combined with air-alcohol concentration could be integrated using the support vector regression classification method to establish a better early warning model, thereby improving vehicle safety
Drunk driving · Medical emergency · Poison control · Support vector machine · Autonomous Vehicle Technology and Safety · Computer Science · Human Factors and Ergonomics · Medicine · Sleep and Work-Related Fatigue · Video Surveillance and Tracking Methods · Artificial Intelligence
| Unique citing works | 2 |
|---|---|
| Citations per year | 0.4 |
| Citation span | 2021 - 2022 (2) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 2 |